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College Menfess Tweet Emotion Project

This project is a Flask web application that predicts the emotions or sentiment of tweets sent to a college menfess account. The application uses machine learning models to analyze and classify the tweets.

Features

  • Emotion Prediction: Predicts the emotion of a tweet using a custom prediction function.
  • Support Vector Machine (SVM) Prediction: Provides an additional prediction using a Support Vector Machine (SVM) model.

Project Structure

.
├── main.py              # The main Flask application script
├── script/
│   └── func.py          # Contains the function `predictTweet` used for predicting the tweet emotion
├── kmeans.py            # Contains the functions `predict_label` and `predict_label_svm` for SVM prediction
├── templates/
│   └── ...              # Directory for HTML templates (if any)
└── README.md            # Project documentation

Getting Started

Prerequisites

  • Python 3.x
  • Flask
  • NumPy
  • Pickle (for model loading)

Installation

  1. Clone the repository:

    git clone https://github.com/akhmadramadani/collegemenfess-emotion.git
cd collegemenfess-emotion

  2. Install the required Python packages:

    pip install -r requirements.txt

Usage

  1. Run the Flask application:

    python main.py

  2. The application will start on http://127.0.0.1:5000/ by default.

API Endpoints

  • GET /: Returns a simple greeting message.

  • POST /predict: Predicts the emotion of the provided tweet. Requires the following form data:

    • tweet: The tweet text to be analyzed.

    Example request:

    curl -X POST -F 'tweet=Your tweet text here' http://127.0.0.1:5000/predict

    Example response:

    {
  "prediction": "positive",
  "predict_using_svm": "neutral"
}

  • POST /predict_svm: Predicts the emotion using the SVM model only. Requires the following form data:

    • tweet: The tweet text to be analyzed.

    Example request:

    curl -X POST -F 'tweet=Your tweet text here' http://127.0.0.1:5000/predict_svm

    Example response:

    {
  "prediction": "neutral"
}

Environment Variables

  • PORT: Set the port for the Flask application. Default is 5000.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

Contributions are welcome! Please submit a pull request or open an issue for any feature requests or bug reports.

Authors


This `README.md` file provides a clear overview of the project, instructions on how to set it up, and details about the API endpoints. Be sure to replace placeholders like `Your Name` and `Your GitHub` with the appropriate details for your project.